The following post is by Dr Brooke Simmons, who has been leading the Zooniverse efforts to help in the aftermath of the recent Caribbean storms.

This year has seen a particularly devastating storm season. As Hurricane Irma was picking up steam and moving towards the Caribbean, we spoke to our disaster relief partners at Rescue Global and in the Machine Learning Research Group at Oxford and decided to activate the Planetary Response Network. We had previously worked with the same partners for our responses to the Nepal and Ecuador earthquakes in 2015 and 2016, and this time Rescue Global had many of the same needs: maps of expected and observed damage, and identifications of temporary settlements where displaced people might be sheltering.

The Planetary Response Network is a partnership with many people and organizations and which uses many sources of data; the Zooniverse volunteers are at its heart. The first cloud-free data available following the storm was of Guadeloupe, and our community examined pre-storm and post-storm images, marking building damage, flooding, impassable roads and signs of temporary structures. The response to our newsletter was so strong that the first set of data was classified in just 2 hours! And as more imaging has become available, we’ve processed it and released it on the project. By the time Hurricane Maria arrived in the Caribbean, Zooniverse volunteers had classified 9 different image sets from all over the Caribbean, additionally including Turks and Caicos, the VirginIslands (US and British), and Antigua & Barbuda. That’s about 1.5 years’ worth of effort, if it was 1 person searching through these images as a full-time job. Even with a team of satellite experts it would still take much longer to analyze what the Zooniverse volunteers collectively have in just days. And there’s still more imaging: the storms aren’t over yet.

We’ve been checking in every day with Rescue Global and our Machine Learning collaborators to get feedback on how our classifications are being used and to refresh the priority list for the next set of image targets. As an example of one of those adjustments, yesterday we paused the Antigua & Barbuda dataset in order to get a rapid estimate of building density in Puerto Rico from images taken just before Irma and Maria’s arrival. We needed those because, while the algorithms used to produce the expected damage maps do incorporate external data like Census population counts and building information from OpenStreetMaps, some of that data can be incomplete or out of date (like the Census, which is an excellent resource but which is many years old now). Our volunteers collectively provided an urgently needed, uniformly-assessed and up-to-date estimate across the whole island in a matter of hours — and that data is now being used to make expected damage maps that will be delivered to Rescue Global before the post-Maria clouds have fully cleared.

Even though the project is still ongoing and we don’t have full results yet, I wanted to share some early results of the full process and the feedback we’ve been getting from responders on the ground. One of our earliest priorities was St. Thomas in the USVI, because we anticipated it would be damaged but other crowdsourcing efforts weren’t yet covering that area. From your classifications we made a raw map of damage markings. Here’s structural damage:

The gray stripe was an area of clouds and some artifacts. You can get an idea from this of where there is significant damage, but it’s raw and still needs further processing. For example, in the above map, damage marked as “catastrophic” is more opaque so will look redder, but more individual markings of damage in the same place will also stack to look redder, so it’s hard to tell the difference in this visualization between 1 building out of 100 that’s destroyed and 100 buildings that all have less severe damage. The areas that had clouds and artifacts also weren’t completely unclassifiable, so there are still some markings in there that we can use to estimate what damage might be lurking under the clouds. Our Machine Learning partners incorporate these classifications and the building counts provided by our project as well as by OpenStreetMaps into a code that produces a “heat map” of structural damage that helps responders understand the probability and proportion of damage in a given area as well as how bad the damage is:

In the heat map, the green areas are where some damage was marked, but at a low level compared to how many buildings are in the area. In the red areas, over 60% of the buildings present were marked as damaged. (Pink areas are intermediate between these.)

With volunteer classifications as inputs, we were able to deliver maps like this (and similar versions for flooding, road blockage, and temporary shelters) for every island we classified. We also incorporated other efforts like those of Tomnod to map additional islands, so that we could keep our focus on areas that hadn’t yet been covered while still providing as much accurate information to responders as possible.

Feedback from the ground has been excellent. Rescue Global has been using the maps to help inform their resource allocation, ranging from where to deliver aid packages to where to fly aerial reconnaissance missions (fuel for flights is a precious commodity, so it’s critical to know in advance which areas most need the extra follow-up). They have also shared the heat maps with other organizations providing response and aid in the area, so Zooniverse volunteers’ classifications are having an extended positive effect on efforts in the whole region. And there has been some specific feedback, too. This message came several days ago from Rebekah Yore at Rescue Global:

In addition to supplying an NGO with satellite communications on St Thomas island, the team also evacuated a small number of patients with critical healthcare needs (including a pregnant lady) to San Juan. Both missions were aided by the heat maps.

To me, this illustrates what we can all do together. Everyone has different roles to play here, from those who have a few minutes a day to contribute to those spending hours clicking and analyzing data, and certainly including those spending hours huddled over a laptop in a temporary base camp answering our emailed questions about project design and priorities while the rescue and response effort goes on around them. Without all of them, none of this would be possible.

We’re still going, now processing images taken following Hurricane Maria. But we know it’s important that our community be able to share the feedback we’ve been receiving, so even though we aren’t finished yet, we still wanted to show you this and say: thank you.

Update:

Now that the project’s active response phase has completed, we have written a further description of how the maps our volunteers helped generate were used on the project’s Results page. Additionally, every registered volunteer who contributed at least 1 classification to the project during its active phase is credited on our Team page. Together we contributed nearly 3 years’ worth of full-time effort to the response, in only 3 weeks.

Further Acknowledgments

The Planetary Response Network has been nurtured and developed by many partners and is enabled by the availability of pre- and post-event imagery. We would like to acknowledge them:

Firstly, our brilliant volunteers. To date on this project we have had contributions from about 10,000 unique IP addresses, of which about half are from registered Zooniverse accounts.

Planet has graciously provided images to the PRN in each of our projects. (Planet Team 2017 Planet Application Program Interface: In Space For Life on Earth. San Francisco, CA. https://api.planet.com, License: CC-BY-SA)

DigitalGlobe provides high-resolution imagery as part of their Open Data Program (Creative Commons Attribution Non Commercial 4.0).

Below is the first in a series of guest blog posts from researchers working on one of our recently launched biomedical projects, Etch A Cell.

Read on to let Dr Martin Jones tell you about the work they’re doing to further understanding of the universe inside our cells!

– Helen

Having trained as a physicist, with many friends working in astronomy, I’ve been aware of Galaxy Zoo and the Zooniverse from the very early days. My early research career was in quantum mechanics, unfortunately not an area where people’s intuitions are much use! However, since I found myself working in biology labs, now at the Francis Crick Institute in London, I have been working in various aspects of microscopy – a much more visual enterprise and one where human analysis is still the gold standard. This is particularly true in electron microscopy, where the busy nature of the images means that many regions inside a cell look very similar. In order to make sense of the images, a person is able to assimilate a whole range of extra context and previous knowledge in a way that computers, for the most part, are simply unable to do. This makes it a slow and labour-intensive process. As if this wasn’t already a hard enough problem, in recent years it has been compounded by new technologies that mean the microscopes now capture images around 100 times faster than before.

Focused ion beam scanning electron microscope

Ten years ago it was more or less possible to manually analyse the images at the same rate as they were acquired, keeping the in-tray and out-tray nicely balanced. Now, however, that’s not the case. To illustrate that, here’s an example of a slice through a group of cancer cells, known as HeLa cells:

We capture an image like this and then remove a very thin layer – sometimes as thin as 5 nanometres (one nanometre is a billionth of a metre) – and then repeat… a lot! Building up enormous stacks of these images can help us understand the 3D nature of the cells and the structures inside them. For a sense of scale, this whole image is about the width of a human hair, around 80 millionths of a metre.

Zooming in to one of the cells, you can see many different structures, all of which are of interest to study in biomedical research. For this project, however, we’re just focusing on the nucleus for now. This is the large mostly empty region in the middle, where the DNA – the instruction set for building the whole body – is contained.

By manually drawing lines around the nucleus on each slice, we can build up a 3D model that allows us to make comparisons between cells, for example understanding whether a treatment for a disease is able to stop its progression by disrupting the cells’ ability to pass on its genetic information.

Animated gif of 3D model of a nucleus

However, images are now being generated so rapidly that the in-tray is filling too quickly for the standard “single expert” method – one sample can produce up to a terabyte of data, made up of more than a thousand 64 megapixel images captured overnight. We need new tricks!

Why citizen science?

With all of the advances in software that are becoming available you might think that automating image analysis of this kind would be quite straightforward for a computer. After all, people can do it relatively easily. Even pigeons can be trained in certain image analysis tasks! (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357). However, there is a long history of underestimating just how hard it is to automate image analysis with a computer. Back in the very early days of artificial intelligence in 1966 at MIT, Marvin Minsky (who also invented the confocal microscope) and his colleague Seymour Papert set the “summer vision project” which they saw as a simple problem to keep their undergraduate students busy over the holidays. Many decades later we’ve discovered it’s not that easy!

Our project, Etch a Cellis designed to allow citizen scientists to draw segmentations directly onto our images in the Zooniverse web interface. The first task we have set is to mark the nuclear envelope that separates the nucleus from the rest of the cell – a vital structure where defects can cause serious problems. These segmentations are extremely useful in their own right for helping us understand the structures, but citizen science offers something beyond the already lofty goal of matching the output of an expert. By allowing several people to annotate each image, we can see how the lines vary from user to user. This variability gives insight into the certainty that a given pixel or region belongs to a particular object, information that simply isn’t available from a single line drawn by one person. Difference between experts is not unheard of unfortunately!

The images below show preliminary results with the expert analysis on the left and a combination of 5 citizen scientists’ segmentations on the right.

Example of expert vs. citizen scientist annotation

In fact, we can go even further to maximise the value of our citizen scientists’ work. The field of machine learning, in particular deep learning, has burst onto the scene in several sectors in recent years, revolutionising many computational tasks. This new generation of image analysis techniques is much more closely aligned with how animal vision works. The catch, however, is that the “learning” part of machine learning often requires enormous amounts of time and resources (remember you’ve had a lifetime to train your brain!). To train such a system, you need a huge supply of so-called “ground truth” data, i.e. something that an expert has pre-analysed and can provide the correct answer against which the computer’s attempts are compared. Picture it as the kind of supervised learning that you did at school: perhaps working through several old exam papers in preparation for your finals. If the computer is wrong, you tweak the setup a bit and try again. By presenting thousands or even millions of images and ensuring your computer makes the same decision as the expert, you can become increasingly confident that it will make the correct decision when it sees a new piece of data. Using the power of citizen science will allow us to collect the huge amounts of data that we need to train these deep learning systems, something that would be impossible by virtually any other means.

We are now busily capturing images that we plan to upload to Etch a cell to allow us to analyse data from a range of experiments. Differences in cell type, sub-cellular organelle, microscope, sample preparation and other factors mean the images can look different across experiments, so analysing cells from a range of different conditions will allow us to build an atlas of information about sub-cellular structure. The results from Etch a cell will mean that whenever new data arrives, we can quickly extract information that will help us work towards treatments and cures for many different diseases.

We recently had a very successful (and longer than usual) Stargazing Live. I wanted to talk a little about the work that our team did in the weeks leading up to this and also recap what actually happened behind the scenes during the two weeks of events.

If you’re not familiar with it, Stargazing Live is an annual astronomy TV show on BBC Two in the UK, which is broadcast live on three consecutive nights. Each year we launch a project in collaboration with the show, and this always proves to be the busiest time of our year. This year, for the first time there was a second week of shows for ABC Australia, so this time we launched two projects instead of one: Planet 9 and Exoplanet Explorers.

A lot of work went into making sure that our site stayed up for this year’s shows. In previous years we’ve had issues that have resulted in either a brief outage or reduced performance for at least some of the time during the show. This year everything worked perfectly and we actually found ourselves reducing our capacity (scaling down) much sooner than we anticipated. The prep work fell into three areas:

Reducing the load on our databases. We reduced the number of requests that result in database queries through caching in the backend (with memcache), and we started using a new microservice (called Designator) to keep track of what each user has seen and serve them new subjects. We also separated some services onto a read replica rather than having them query the primary database.

Adding feature flags so that we could turn off anything non-essential, and so that we could shut down any features that were causing problems, using the Flipper Ruby gem.

The Oxford team gathers in the office to watch the show.

On the first night of the BBC show it was all hands on deck. Our teams in the US and the UK were in our offices, despite it being evening in the UK, and in Oxford we gathered around the TV expectantly awaiting the moment when Chris would announce the project’s URL on air. That moment is usually a bit frantic, as several thousand people all turn up on the site at once and start clicking around, registering, logging in, and submitting classifications. We’re always closely watching our monitoring systems, keeping an eye on various performance metrics, watching for any early signs of problems that might affect the performance of the site. This year when that moment came the number of visitors on site shot up to over 5,000, and then… everything just kept running smoothly.

The first night of the BBC show we peaked at about 0.9 million requests per hour, with 1.1 million per hour the second night.

Requests to Zooniverse.org during BBC Stargazing Live 2017.

We scaled our API service to 50 of EC2’s m3.medium instances the first night and the average CPU utilisation of these instances reached about 30% at peak traffic. The next two nights we reduced the number of instances to 40. In hindsight we could have gone even lower, but from past experience the amount of traffic we receive on the second and third nights can be difficult to predict, so we decided to play it safe.

API scaling and CPU utilisation during BBC Stargazing Live 2017.

Traffic during the ABC show was lower than during the BBC show (Australia has a smaller population than the UK, so this was as expected). That week we scaled the API to 40 instances the first night, and 20 instances for the second and third nights.

In the past we’ve had problems with running out of available connections in PostgreSQL. The connection limit depends on available memory, and we find this to be more of a problem than CPU or network constraints. During the shows we scaled the PostgreSQL instance for our main API to RDS’s m4.10xlarge and our Talk/microservices database to m4.2xlarge, primarily to give us enough leeway to avoid the connection limit. In the future we’d like to implement connection pooling to avoid this.

This was all a big improvement on previous years. While before we found ourselves extremely busy fighting fires and fixing bugs between shows, this time we had time to just relax and watch the show. We have more work to do on optimisations, because we did still have to scale up our capacity more than we’d like, but overall we’re very happy with how well things went this year.

Breaking news… Zooniverse volunteers on Exoplanet Explorers have discovered a new 4-planet system!

Computer animation of the 4-planet system. Planet orbits are to scale and planet sizes are to scale with each other, but not with the star and the size of the orbits. Credit: Simone Duca.

Congratulations to all* who directly classified the light curves for this system, bringing it to the attention of the research team. And an enormous *thank you* to the 14,000+ volunteers who provided over 2 million classifications in just three days to make this discovery possible. This is equivalent to 3.4 years of full time effort. I *heart* people-powered research! It’s also amazing how quickly we were able to get these data to the eyes of the public — the Kepler Space Satellite observed this star between December 15 and March 4, 2017. Data arrived on Earth on March 7th and Zooniverse volunteers classified it April 3-5, 2017. I *heart* Zooniverse.

ExoplanetExplorers.org was the featured project for our inaugural ABC Australia Stargazing Live 3-day, prime-time TV event, which just ended yesterday and through which this discovery was made. Over the years, we’ve partnered with the BBC as part of their Stargazing Live event in the UK. On night 1, Chris Lintott, our intrepid leader, invites the million+ viewers to participate in that year’s featured Zooniverse project, on night 2 he highlights interesting potential results coming through the pipeline, and on night 3, if science nods in our favor, he has the pleasure of announcing exciting discoveries you all, our volunteers, have made (for example, last year’s pulsar discovery and the supernova discovery from a couple years back).

This year we partnered with both the UK’s BBC and Australia’s ABC TV networks to run two Stargazing Live series in two weeks. We’re exhausted and exhilarated from the experience! We can imagine you all are as well (hats off to one of our volunteers who provided over 15,000 classifications in the first two days)!

Stargazing Live epitomizes many of our favorite aspects of being a member of the Zooniverse team – it’s a huge rush, filled with the highs and lows of keeping a site up when thousands of people are suddenly providing ~7000 classifications a minute at peak. We’re so proud of our web development team and their amazing effort; their smart solutions, quick thinking, and teamwork. The best part is that we collectively get to experience the joy, wonder, and discovery of the process of science right alongside the researchers. Each year the research teams leading each project have what is likely among the most inspiring (and intense) 3-days of their careers, carrying out the detective work of following up each potential discovery at breakneck speed.

Over 2 million classifications in just 1 day on planetninesearch.org!

Brad Tucker and his team leading PlanetNineSearch.org featured in the BBC Stargazing Live event this year checked and rechecked dozens of Planet 9 candidates orbital parameters and against known object catalogs, making sure no stone was left unturned. We were bolstered throughout with re-discoveries of known objects, including many known asteroids and Chiron, a minor planet in the outer Solar System, orbiting the Sun between Saturn and Uranus.

The red, green, and blue dots in the lower left quadrant show Chiron as it moved across the Australian night sky during the Skymapper Telescope Observations for planetninesearch.org.

Even though Planet 9 hasn’t been discovered yet, it’s huge progress for that field of research to have completed a thorough search through this Skymapper dataset, which allows us to probe out to certain distances and sizes of objects across a huge swath of the sky. Stay tuned for progress at planetninesearch.org and through the related BackyardWorlds.org project, searching a different parameter space for Planet 9 in WISE data.

Also, and very importantly, the BBC Stargazing Live shows gave the world an essential new member of the Twitterverse:

The Exoplanet Explorers team, led by Ian Crossfield, Jessie Christiansen, Geert Barentsen, Tom Barclay, and more were also up through much of each night of the event this week, churning through the results. Because the Kepler Space Telescope K2 dataset is so rich, there were dozens of potential candidates to triple check in just 3 days. Not only did our volunteers discover the 4-planet system shown above, but 90 new and true candidate exoplanets! That’s truly an amazing start to a project.

Chris Lintott shows Brian Cox and Julia Zemiro the possible planets we’ve found so far, using the nearby town’s entire stock of gumballs.

Once you all, our amazing community, have classified all the images in this project and the related PlanetHunters.org, the researchers will be able to measure the occurrence rates of different types of planets orbiting different types of stars. They’ll use this information to answer questions like — Are small planets (like Venus) more common than big ones (like Saturn)? Are short-period planets (like Mercury) more common than those on long orbits (like Mars)? Do planets more commonly occur around stars like the Sun, or around the more numerous, cooler, smaller “red dwarfs”?

There’s also so much to learn about the 4-planet system itself. It’s particularly interesting because it’s such a compact system (all orbits are well within Mercury’s distance to our Sun) of potentially rocky planets. If these characteristics hold true, we expect they will put planet formation theories to the test.

A fun part of our effort for the show was to create visualizations for this newly discovered system. Simone, one of our developers, used http://codepen.io/anon/pen/RpOYRw to create the simulation shown above. We welcome all to try their hand using this tool or others to create their favorite visualization of the system. Do post your effort in the comments below. To set you on the right path, here are our best estimates for the system so far:

The star is in the constellation of Aquarius (see if can get the WWT), with ra, dec = 23:15:47.77, -10:50:58.91.

Host star (V=12): 0.8 Rsol, 0.9 Msol. Late G or early K.

Rotates once every 12 days or so.

Distance: 183pc = 597ly.

Sizes: 1.98 Re, 2.03 Re, 2.74 Re, 2.22 Re.

Periods: 3.6d, 5.4d, 8.3d, 12.8d.

Semi-major axii: 0.04 AU, 0.06 AU, 0.08 AU, 0.10 AU.

We predict there may be more planets further out, with similar resonances as the inner planets. The predictions for outer planets are 20d, 30.7d, 47d, etc. (assuming Per_x = 3.56 * 1.538^x.). Planet number 11 would be ~264d, planet 12 ~405d.

There are 73 other previously discovered exoplanet systems with 4 or more planets known.

In 2372 years, on July 9, 4388AD, all four planets will transit at the same time.

If you’re standing on planet e, the nearest planet would appear bigger than the full moon on the sky. Apparent size of other planets while standing on e = 10 arcmin, 16 arcmin, 32 arcmin.

If you’re on planet e, the star barely appears to rotate: you see the same side of it for many “years,” because the star rotates just as quickly as planet “e” goes around it.

This post wouldn’t be complete without a thank you to Edward Gomez for following up candidates with the Los Cumbres Observatory Robotic Telescope Network. Not only is LCO a great research tool, but it provides amazing access to telescopes and quality curricular materials for students around the world.

Below is a guest post from a researcher who has been studying the Zooniverse and who just published a paper called ‘Crowdsourced Science: Sociotechnical epistemology in the e-research paradigm’. That being a bit of a mouthful, I asked him to introduce himself and explain – Chris.

My name is David Watson and I’m a data scientist at Queen Mary University of London’s Centre for Translational Bioinformatics. As an MSc student at the Oxford Internet Institute back in 2015, I wrote my thesis on crowdsourcing in the natural sciences. I got in touch with several members of the Zooniverse team, who were kind enough to answer all my questions (I had quite a lot!) and even provide me with an invaluable dataset of aggregated transaction logs from 2014. Combining this information with publication data from a variety of sources, I examined the impact of crowdsourcing on knowledge production across the sciences.

Last week, the philosophy journal Synthese published a (significantly) revised version of my thesis, co-authored by my advisor Prof. Luciano Floridi. We found that Zooniverse projects not only processed far more observations than comparable studies conducted via more traditional methods—about an order of magnitude more data per study on average—but that the resultant papers vastly outperformed others by researchers using conventional means. Employing the formal tools of Bayesian confirmation theory along with statistical evidence from and about Zooniverse, we concluded that crowdsourced science is more reliable, scalable, and connective than alternative methods when certain common criteria are met.

We were surprised by several things in our research, however. First, the significance of the disparity between the performance of publications by Zooniverse and those by other labs was greater than expected. This plot represents the distribution of citation percentiles by year and data source for articles by both groups. Statistical tests confirm what your eyes already suspect—it ain’t even close.

We were also impressed by the networks that appear in Zooniverse projects, which allow users to confer with one another and direct expert attention toward particularly anomalous observations. In several instances this design has resulted in patterns of discovery, in which users flag rare data that go on to become the topic of new projects. This structural innovation indicates a difference not just of degree but of kind between so-called “big science” and crowdsourced e-research.

If you’re curious to learn more about our study of Zooniverse and the site’s implications for sociotechnical epistemology, check out our complete article.

We’re testing out a new feature of our interface, which means if you’re classifying images on Comet Hunters you may see occasional pop-up messages like the one pictured above.

The messages are designed to give you more information about the project. If you do not want to see them, you have the option to opt-out of seeing any future messages. Just click the link at the bottom of the pop-up.

You can have a look at this new feature by contributing some classifications today at www.comethunters.org.

We’re cleaning up our email list to make sure that we do not email anyone who does not want to hear from us. You will have got an email last week asking you if you want to stay subscribed. If you did not click the link in that email, then you will have received one today saying you have been unsubscribed from our main mailing list. Don’t worry! If you still want to receive notifications from us regarding things like new projects, please go to www.zooniverse.org/settings/email and make sure you’re subscribed to general Zooniverse email updates.
NOTE: This has not affected emails you get from individual Zooniverse projects.

The AsteroidZoo community has exhausted the data that are available at this time. With all the data examined we are going to pause the experiment, and before users spend more time we want to make sure that we can process your finds through the Minor Planet Center and get highly reliable results.

We understand that it’s frustrating when you’ve put in a lot of work, and there isn’t a way to confirm how well you’ve done. But please keep in mind that this was an experiment – How well do humans find asteroids that machines cannot?

Often times in science an experiment can run into dead-ends, or speed-bumps; this is just the nature of science. There is no question that the AsteroidZoo community has found several potential asteroid candidates that machines and algorithms simply missed. However, the conversion of these tantalizing candidates into valid results has encountered a speed bump.

What’s been difficult is that all the processing to make an asteroid find “real” has been based on the precision of a machine – for example the arc of an asteroid must be the correct shape to a tiny fraction of a pixel to be accepted as a good measurement. The usual process of achieving such great precision is hands-on, and might take takes several humans weeks to get right. On AsteroidZoo, given the large scale of the data, automating the process of going from clicks to precise trajectories has been the challenge.

While we are paused, there will be updates to both the analysis process, and the process of confirming results with the Minor Planet Center. Updates will be posted as they become available.

My name is Dr. Karen Masters, and I’m an astronomer working at the University of Portsmouth. My main involvement with the Zooniverse over the last 8 years or so has been through my research into galaxy evolution making use of the Galaxy Zoo classifications (see Zooniverse Publication list), and as the Project Scientist for Galaxy Zoo I enjoy organizing science team telecons, and research meetings. I’ve also written many blog posts about galaxy evolution for the Galaxy Zoo blog.

Being involved in Galaxy Zoo has opened many interesting doors for me. I have always had a keen interest in science communication and science education. In fact, working with Galaxy Zoo has been a real pleasure because of the way it blurred the lines between astronomical research and public engagement.

A couple of years ago I was given the opportunity to get more formally engaged in researching how Galaxy Zoo (and other Zooniverse projects) contribute to science communication/education. A colleague of mine in the Portsmouth Business School, who is an expert in the economics of volunteering, led a team (which I was part of) which was successful in obtaining funding for a 3 year project to study the motivations of citizen scientists, including how scientific learning contributes to the motivations. We call our project VOLCROWE.

The VOLCROWE survey, which ran in late March/early April of last year included a science quiz, which tested both general science knowledge, and knowledge specific to five different surveys. This meant that the data collected could be used to investigate, in a statistical sense, how much you are learning about scientific content while classifying on Zooniverse projects.

The survey respondents certainly believed they were learning about science through their participation. When asked if they Zooniverse (i) lets them learn through direct hands on experience of scientific research; (ii) allows them to gain a new perspective on scientific research; or (iii) helps them learn about science, and overwhelming majority (more than 80% in all cases) agreed, or strongly agreed.

Responses to questions about if the volunteers agreed that the Zooniverse…..

We were also able to find evidence in the survey responses that project specific science knowledge correlated positively with measures of active engagement in the project. Put plainly, people who classified more on a given project we found to know more about the scientific content of that project. We could use the scores from the general science quiz as a measure of unrelated scientific knowledge (which did not correlate with how much people classified) to claim that this correlation is causal – i.e. people are learning more about the science behind our projects the more time they spend classifying.

A different VOLCROWE publication, “How is success defined and measured in online citizen science? A case study of Zooniverse projects”, Cox et al. (2015), measured the success of Zooniverse projects in different metrics. In that work we demonstrated that projects could be scientifically successful (i.e. contribute to increased scientific output) without being very successful in public engagement. However, public engagement success without good scientific output was not found in any of the Zooniverse projects studied in Cox et al. (2015). Four of our five projects in our Science Learning study were part of Cox et al. (2015; Penguin Watch hadn’t launched at that time) and in Masters et al. (2016) we were able to show that the better projects did in public engagement success metrics, in general the stronger the correlation we found between scientific knowledge and time spent classifying. This does not seem too surprising, but it’s nice to show with data.

We concluded thus:

“Our results imply that even for citizen science project designed primarily to meet the research goals of a science team, volunteers are learning about scientific topics while participating. Combined with previous work (Cox et al. 2015) that suggested it is difficult for projects to be successful at public engagement without being scientifically successful (but not vice versa) this has implications for future design of citizen science projects, even those primarily motivated by public engagement aims. While scientific success will not alone lead to scientific learning among the user community, we argue that these works together demonstrate scientific success is a necessary (if not a sufficient) requirement for successful and sustainable public engagement through citizen science. We conclude that the best way to use citizen science projects to provide an environment that facilitates science learning is to provide an authentic science driven project, rather than to develop projects with solely educational aims.”

As you may know, authenticity is at the heart of the Zooniverse Philosophy, so it was really nice to find this evidence which backs that up. You know you can trust Zooniverse projects to make use of your classifications to make contributions to the sum of knowledge of humankind.

I also had great fun writing this up for publication, a process which involved me learning a great deal about what is meant by “Science Learning” in the context of research into science communication.

We’re getting through the first round of Penguin Watch data- it’s amazing and it’s doing the job we wanted, which is to revolutionise the collection and processing of penguin data from the Southern Ocean – to disentangle the threats of climate change, fishing and direct human disturbance. The data are clearly excellent, but we’re now trying to automate processing them so that results can more rapidly influence policy.

In “PenguinWatch 2.0”, people will be able to see the results of their online efforts to monitor and conserve Antarctica’s penguins colonies. The more alert among you will notice that it’s not fully there yet, but we’re working on it!

We have loads of ideas on how to integrate this with the penguinwatch.org experience so that people are more engaged, learn more and realise what they are contributing to!

For now, we’re doing this the old-fashioned way; anyone such as schools who want to be more engaged, can contact us (tom.hart@zoo.ox.ac.uk) and we’ll task you with a specific colony and feedback on that.